Merge branch 'master' into ridgeline-size

Author: tjmahr

DESCRIPTION

@@ -1,15 +1,16 @@
 Package: bayesplot
 Type: Package
 Title: Plotting for Bayesian Models
-Version: 1.7.2.9000
-Date: 2020-05-27
+Version: 1.8.0.9000
+Date: 2021-01-07
 Authors@R: c(person("Jonah", "Gabry", role = c("aut", "cre"), email = "[email protected]"),
              person("Tristan", "Mahr", role = "aut"),
              person("Paul-Christian", "Bürkner", role = "ctb"),
              person("Martin", "Modrák", role = "ctb"),
              person("Malcolm", "Barrett", role = "ctb"), 
              person("Frank", "Weber", role = "ctb"),
-             person("Eduardo", "Coronado Sroka", role = "ctb"))
+             person("Eduardo", "Coronado Sroka", role = "ctb"),
+             person("Aki", "Vehtari", role = "ctb"))
 Maintainer: Jonah Gabry <[email protected]>
 Description: Plotting functions for posterior analysis, MCMC diagnostics,
     prior and posterior predictive checks, and other visualizations 
@@ -21,7 +22,7 @@ Description: Plotting functions for posterior analysis, MCMC diagnostics,
     particularly (but not exclusively) packages interfacing with 'Stan'.
 License: GPL (>= 3)
 LazyData: TRUE
-URL: https://mc-stan.org/bayesplot
+URL: https://mc-stan.org/bayesplot/
 BugReports: https://github.com/stan-dev/bayesplot/issues/
 SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
 Depends:

NAMESPACE

@@ -104,7 +104,9 @@ export(ppc_boxplot)
 export(ppc_data)
 export(ppc_dens)
 export(ppc_dens_overlay)
+export(ppc_dens_overlay_grouped)
 export(ppc_ecdf_overlay)
+export(ppc_ecdf_overlay_grouped)
 export(ppc_error_binned)
 export(ppc_error_hist)
 export(ppc_error_hist_grouped)

NEWS.md

@@ -1,39 +1,47 @@
 <!-- See http://style.tidyverse.org/news.html for advice on writing news -->
 
+# bayesplot 1.8.0.9000
 
-# bayesplot 1.7.2.9000
+Items for next release go here
 
-<!--
-(GitHub issue/PR numbers in parentheses)
-* Items for next release go here
--->
-
-* On the y axis, `ppc_loo_pit_qq(..., compare = "normal")` now plots standard
-  normal quantiles calculated from the PIT values (instead of the standardized
-  PIT values). (#240, #243, @fweber144)
+* Size of points and interval lines can set in 
+  `mcmc_intervals(..., outer_size, inner_size, point_size)`. (#215, #228, #229) 
+ 
+# bayesplot 1.8.0
 
-* New plotting function `ppc_km_overlay()` for outcome variables that are   
-  right-censored. Empirical CCDF estimates of `yrep` are compared with the 
-  Kaplan-Meier estimate of `y`. (#233, #234, @fweber144)
+### Bug fixes
 
-* CmdStanMCMC objects (from CmdStanR) can now be used with extractor 
-  functions `nuts_params()`, `log_posterior()`, `rhat()`, and 
-  `neff_ratio()`. (#227)
+* `mcmc_areas()` tries to use less vertical blank space. (#218, #230)
 
-* Size of points and interval lines can set in 
-  `mcmc_intervals(..., outer_size, inner_size, point_size)`. (#215, #228, #229) 
-  
-* Size of ridgelines can be set in `mcmc_areas(..., size)` and `mcmc_areas_ridges(..., size)`. (#224)
-  
-* `mcmc_areas()` tries to use less blank vertical blank space. (#218, #230) 
+### New features
 
-* Added missing `facet_args` argument to `mcmc_rank_overlay()`. (#221, @hhau)
+* New plotting functions `ppc_dens_overlay_grouped()` and `ppc_ecdf_overlay_grouped()`
+  for plotting density and cumulative distributions of the posterior predictive
+  distribution (versus observed data) by group. (#212)
 
+* New plotting function `ppc_km_overlay()` for outcome variables that are   
+  right-censored. Empirical CCDF estimates of `yrep` are compared with the
+  Kaplan-Meier estimate of `y`. (#233, #234, @fweber144)
+  
 * `ppc_loo_pit_overlay()` now uses a boundary correction for an improved kernel
   density estimation. The new argument `boundary_correction` defaults to TRUE but
   can be set to FALSE to recover the old version of the plot. (#171, #235,
   @ecoronado92)
 
+* CmdStanMCMC objects (from CmdStanR) can now be used with extractor
+  functions `nuts_params()`, `log_posterior()`, `rhat()`, and
+  `neff_ratio()`. (#227)
+  
+* On the y axis, `ppc_loo_pit_qq(..., compare = "normal")` now plots standard
+  normal quantiles calculated from the PIT values (instead of the standardized
+  PIT values). (#240, #243, @fweber144)
+
+* `mcmc_rank_overlay()` gains argument `facet_args`. (#221, @hhau)
+
+* For `mcmc_intervals()` the size` of the points and interval lines can be set with
+  `mcmc_intervals(..., outer_size, inner_size, point_size)`. (#215, #228, #229)
+
+
 
 # bayesplot 1.7.2
 
@@ -54,39 +62,39 @@ matrices also inheriting from "array" in R 4.0.
   examples. (#161, #183, #188)
 
 * Two new plots have been added for inspecting the distribution of ranks.
-  Rank histograms were introduced by the Stan team's [new paper on 
+  Rank histograms were introduced by the Stan team's [new paper on
   MCMC diagnostics](https://arxiv.org/abs/1903.08008). (#178, #179)
 
   `mcmc_rank_hist()`: A traditional traceplot (`mcmc_trace()`) visualizes how
   sampled values the MCMC chains mix over the course of sampling. A rank
   histogram (`mcmc_rank_hist()`) visualizes how the *ranks* of values from the
   chains mix together. An ideal plot would show the ranks mixing or overlapping
   in a uniform distribution.
-  
+
   `mcmc_rank_overlay()`: Instead of drawing each chain's histogram in a separate
   panel, this plot draws the top edge of the chains' histograms in a single
   panel.
-  
+
 * Added `mcmc_trace_data()`, which returns the data used for plotting the trace
   plots and rank histograms. (Advances #97)
 
-* [ColorBrewer](http://colorbrewer2.org) palettes are now available as color
+* [ColorBrewer](https://colorbrewer2.org/) palettes are now available as color
   schemes via
   [`color_scheme_set()`](https://mc-stan.org/bayesplot/reference/bayesplot-colors.html).
-  For example, `color_scheme_set("brewer-Spectral")` will use the Spectral 
+  For example, `color_scheme_set("brewer-Spectral")` will use the Spectral
   palette. (#177, #190)
 
-* MCMC plots now also accept objects with an `as.array` method as 
+* MCMC plots now also accept objects with an `as.array` method as
   input (e.g., stanfit objects). (#175, #184)
 
 * [`mcmc_trace()`](https://mc-stan.org/bayesplot/reference/MCMC-traces.html)
   gains an argument `iter1` which can be used to label the traceplot starting
   from the first iteration after warmup. (#14, #155, @mcol)
 
 * [`mcmc_areas()`](https://mc-stan.org/bayesplot/reference/MCMC-intervals.html)
-  gains an argument `area_method` which controls how to draw the density 
-  curves. The default `"equal area"` constrains the heights so that the curves 
-  have the same area. As a result, a narrow interval will appear as a spike 
+  gains an argument `area_method` which controls how to draw the density
+  curves. The default `"equal area"` constrains the heights so that the curves
+  have the same area. As a result, a narrow interval will appear as a spike
   of density, while a wide, uncertain interval is spread thin over the _x_ axis.
   Alternatively `"equal height"` will set the maximum height on each curve to
   the same value. This works well when the intervals are about the same width.
@@ -113,12 +121,12 @@ matrices also inheriting from "array" in R 4.0.
 * The examples in
   [`?ppc_loo_pit_overlay()`](https://mc-stan.org/bayesplot/reference/PPC-loo.html)
   now work as expected. (#166, #167)
-  
-* Added `"viridisD"` as an alternative name for `"viridis"` to the supported 
+
+* Added `"viridisD"` as an alternative name for `"viridis"` to the supported
   colors.
 
-* Added `"viridisE"` (the [cividis](https://github.com/marcosci/cividis) 
-  version of viridis) to the supported colors. 
+* Added `"viridisE"` (the [cividis](https://github.com/marcosci/cividis)
+  version of viridis) to the supported colors.
 
 * `ppc_bars()` and `ppc_bars_grouped()` now allow negative integers as input.
   (#172, @jeffpollock9)
@@ -161,7 +169,7 @@ matrices also inheriting from "array" in R 4.0.
   gains an argument `discrete`, which is `FALSE` by default, but can be used
   to make the Geom more appropriate for discrete data. (#145)
 
-* [PPC intervals 
+* [PPC intervals
   plots](https://mc-stan.org/bayesplot/reference/PPC-intervals.html) and [LOO
   predictive checks](https://mc-stan.org/bayesplot/reference/PPC-loo.html) now
   draw both an outer and an inner probability interval, which can be

R/bayesplot-colors.R

@@ -230,8 +230,9 @@ plot_scheme <- function(scheme = NULL) {
     legend_none() +
     xaxis_text(
       face = "bold",
-      margin = margin(t = -3, b = 10),
+      margin = margin(t = -3, b = 10, unit = "pt"),
       angle = 0,
+      vjust = 1,
       debug = FALSE
     )
 }

R/bayesplot-package.R

@@ -44,7 +44,7 @@
 #'
 #' @section Resources:
 #' * __Online documentation and vignettes__: Visit the __bayesplot__ website at
-#'   <https://mc-stan.org/bayesplot>
+#'   <https://mc-stan.org/bayesplot/>
 #' * __Bug reports and feature requests__: If you would like to request a new
 #'   feature or if you have noticed a bug that needs to be fixed please let us
 #'   know at the **bayesplot** issue tracker at

R/mcmc-diagnostics.R

@@ -580,8 +580,7 @@ acf_data <- function(x, lags) {
     abort(paste0("Too few iterations for lags=", lags, "."))
   }
 
-  data <- reshape2::melt(x, value.name = "Value")
-  data$Chain <- factor(data$Chain)
+  data <- melt_mcmc(x)
   ac_list <- tapply(
     data[["Value"]],
     # INDEX = list(data[["Chain"]], data[["Parameter"]]),

R/mcmc-intervals.R

@@ -94,6 +94,19 @@
 #'   expand = expansion(add = c(.1, .3))
 #' )
 #'
+#' # relabel parameters
+#' p + scale_y_discrete(
+#'   labels = c("alpha" = "param label 1",
+#'              "beta[4]" = "param label 2")
+#')
+#'
+#' # relabel parameters and define the order
+#' p + scale_y_discrete(
+#'   labels = c("alpha" = "param label 1",
+#'              "beta[4]" = "param label 2"),
+#'   limits = c("beta[4]", "alpha")
+#' )
+#'
 #' # color by rhat value
 #' color_scheme_set("blue")
 #' fake_rhat_values <- c(1, 1.07, 1.3, 1.01, 1.15, 1.005)

R/ppc-censoring.R

@@ -5,12 +5,11 @@
 #'   predictive distribution. See the **Plot Descriptions** section, below, for
 #'   details.
 #'
-#'   Although some of the other plots can be used with censored data,
-#'   `ppc_km_overlay()` is currently the only plotting function designed
+#'   Although some of the other \pkg{bayesplot} plots can be used with censored
+#'   data, `ppc_km_overlay()` is currently the only plotting function designed
 #'   *specifically* for censored data. We encourage you to suggest or contribute
-#'   additional plots at [https://github.com/stan-dev/bayesplot](github.com/stan-dev/bayesplot).
-#'
-#'
+#'   additional plots at
+#'   [github.com/stan-dev/bayesplot](https://github.com/stan-dev/bayesplot).
 #'
 #' @name PPC-censoring
 #' @family PPCs
@@ -26,10 +25,10 @@
 #' \describe{
 #'   \item{`ppc_km_overlay()`}{
 #'    Empirical CCDF estimates of each dataset (row) in `yrep` are overlaid,
-#'    with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for `y` itself
-#'    on top (and in a darker shade). This is a PPC suitable for
-#'    right-censored `y`. Note that the replicated data from `yrep` is assumed
-#'    to be uncensored.
+#'    with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for `y` itself on
+#'    top (and in a darker shade). This is a PPC suitable for right-censored
+#'    `y`. Note that the replicated data from `yrep` is assumed to be
+#'    uncensored.
 #'   }
 #' }
 #'
@@ -74,11 +73,9 @@ ppc_km_overlay <-
       abort("Package 'ggfortify' required.")
     }
 
-    # Checks for 'status_y':
     stopifnot(is.numeric(status_y))
     stopifnot(all(status_y %in% c(0, 1)))
 
-    # Create basic PPC dataset:
     data <- ppc_data(y, yrep, group = status_y)
 
     # Modify the status indicator:
@@ -93,23 +90,20 @@ ppc_km_overlay <-
                                    as.numeric(as.character(.data$group)),
                                    1))
 
-    # Create 'survfit' object and 'fortify' it
     sf <- survival::survfit(
       survival::Surv(value, group) ~ rep_label,
       data = data
     )
     fsf <- fortify(sf)
 
-    # Add variables specifying color, size, and alpha:
     fsf$is_y_color <- as.factor(sub("\\[rep\\] \\(.*$", "rep", sub("^italic\\(y\\)", "y", fsf$strata)))
     fsf$is_y_size <- ifelse(fsf$is_y_color == "yrep", size, 1)
     fsf$is_y_alpha <- ifelse(fsf$is_y_color == "yrep", alpha, 1)
 
     # Ensure that the observed data gets plotted last by reordering the
-    # levels of the factor "strata":
+    # levels of the factor "strata"
     fsf$strata <- factor(fsf$strata, levels = rev(levels(fsf$strata)))
 
-    # Plot:
     ggplot(data = fsf,
            mapping = aes_(x = ~ time,
                           y = ~ surv,